{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "promising-might",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dried-physics",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import random as rd\n",
    "rd.randint(1, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "following-windows",
   "metadata": {},
   "outputs": [],
   "source": [
    "name = 'zhangsan'\n",
    "score = 98\n",
    "name2 = 'maishu'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "satisfactory-subject",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fc47f25f550>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(np.random.randn(50).cumsum())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "dependent-photograph",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run plot.py"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "sustainable-register",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "for i in range(5):\n",
      "    print(i)\n",
      "import random as rd\n",
      "rd.randint(10)\n",
      "import random as rd\n",
      "rd.randint(1, 10)\n",
      "import numpy as np\n",
      "import matplotlib.plot as plt\n",
      "plt.plot(np.random.randn(50).cumsum())\n",
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "plt.plot(np.random.randn(50).cumsum())\n",
      "%run plot.py\n",
      "%hist\n"
     ]
    }
   ],
   "source": [
    "%hist"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "incredible-poker",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "491 ns ± 108 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit a = range(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "anonymous-checklist",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "25.5 µs ± 2.02 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit a = list(range(1000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "annoying-petite",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
